• Journal of the Chungcheong Mathematical Society
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• v.25 no.1
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• pp.51-63
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• 2012

In this paper, we prove the stability in random normed spaces via fixed point method for the functional equation $$f(2x+y)+f(2x-y)+2f(x)-f(x+y)-f(x-y)-2f(2x)=0$$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7B
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• pp.818-826
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• 2011

This paper proposes a novel FTL (Flash Translation Layer) algorithm employing sector-level mapping technique based on locality to reduce the number of erase operations in flash memory accesses. Sector-level mapping technique shows higher performance than other mapping techniques, even if it requires a large mapping table. The proposed algorithm reduces the size of mapping table by employing dynamic table update, processes sequential writes by exploiting sequential locality and extracts hot sector in random writes. Experimental results show that the number of erase operations has been reduced by 75.4%, 65.8%, and 10.3% respectively when compared with well-known BAST, FAST and sector mapping algorithms.

• Communications of the Korean Mathematical Society
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• v.31 no.1
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• pp.147-161
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• 2016

In this paper, we study the strong convergence and stability of a new two step random iterative scheme with errors for accretive Lipschitzian mapping in real Banach spaces. The new iterative scheme is more acceptable because of much better convergence rate and less restrictions on parameters as compared to random Ishikawa iterative scheme with errors. We support our analytic proofs by providing numerical examples. Applications of random iterative schemes with errors to variational inequality are also given. Our results improve and establish random generalization of results obtained by Chang [4], Zhang [31] and many others.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.5
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• pp.15-24
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• 2009

This paper proposes an effective low power BIST architecture using the pattern mapping method for 100% fault coverage and the transition freezing method for making high correlative low power patterns. When frozen patterns are applied to a circuit, it begins to find a great number of faults at first. However, patterns have limitations of achieving 100% fault coverage due to random pattern resistant faults. In this paper, those faults are covered by the pattern mapping method using the patterns generated by an ATPG and the useless patterns among frozen patterns. Throughout the scheme, we have reduced an amount of applied patterns and test time compared with the transition freezing method, which leads to low power dissipation.

• BMB Reports
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• v.35 no.5
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• pp.465-471
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• 2002

From fruiting bodies of L. edodes strain L-54, single-spore isolates (SSIs) were collected. Two parental types of L-54 were regenerated via monokaryotization. By means of random-amplified polymorphic DNA (RAPD), DNA samples from L-54, its two parental types, and 32 SSIs were amplified with arbitrary primers. Dedikaryotization was demonstrated, and 91 RAPD-based molecular markers were generated. RAPD markers that were segregated at a 1:1 ratio were used to construct a linkage map of L. edodes. This RAPD-linkage map greatly enhanced the mapping of other inheritable and stable markers [such as those that are linked to a phenotype (the mating type), a known gene (priA) and a sequenced DNA fragment (MAT)] with the aid of mating tests, bulked-segregant analysis, and PCR-single-strand conformational polymorphism. These markers comprised a genetic map of L. edodes with 14 linkage groups and a total length of 622.4 cM.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.375-386
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• 2022

The city of Khartoum, the capital of Sudan, was heavily damaged by the flood of the Nile in 2020. Classification using satellite images can define the damaged area and help emergency response. As Synthetic Aperture Radar (SAR) uses microwave that can penetrate cloud, it is suitable to use in the flood study. In this study, Random Forest classifier, one of the supervised classification algorithms, was applied to the flood event in Khartoum with various sizes of the training dataset and number of images using Sentinel-1 SAR. To create a training dataset, we used unsupervised classification and visual inspection. Firstly, Random Forest was performed by reducing the size of each class of the training dataset, but no notable difference was found. Next, we performed Random Forest with various number of images. Accuracy became better as the number of images in creased, but converged to a maximum value when the dataset covers the duration from flood to the completion of drainage.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.918-922
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• 2009

Naturally, block mapping FTL works well for sequential writes while page mapping FTL does well for random writes. To exploit their advantages, a practical FTL should be able to selectively apply a suitable scheme between page and block mappings for each write pattern. To meet that requirement, we propose a hybrid mapping FTL, which we call Janus-FTL, that distributes data to either block or page mapping areas. Also, we propose the fusion operation to relocate the data from block mapping area to page mapping area and the defusion operation to relocate the data from page mapping area to block mapping area. And experimental results of Janus-FTL show performance improvement of maximum 50% than other hybrid mapping FTLs.

• Steel and Composite Structures
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• v.46 no.3
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• pp.435-449
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• 2023

Time domain method and frequency domain method are commonly used in the current fatigue life calculation theory. The time domain method has complicated procedures and needs a large amount of calculation, while the frequency domain method has poor applicability to different materials and different spectrum, and improper selection of spectrum model will lead to large errors. Considering that artificial neural network has strong ability of nonlinear mapping and generalization, this paper applied this technique to random fatigue life prediction, and the effect of average stress was taken into account, thereby achieving more accurate prediction result of random fatigue life.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.9
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• pp.1250-1256
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• 2002

A novel fine-mapping method exploiting linkage disequilibrium (LD) was applied to better refine the quantitative trait loci (QTL) positions for milk production traits on bovine chromosome 14 in the pedigree comprising 22 paternal half-sib families of a Black-and-White Holstein-Friesian grand-daughter design in the Netherlands for a total of 1,034 sons. The chromosome map was constructed with the 31 genetic markers spanning 90 Kosambi cM with the average inter-marker distance of 3.5 cM. The linkage analyses, in which the effects of sire QTL alleles were assumed random and the random factor of the QTL allelic effects was incorporated into the Animal Model, found the QTL for milk, fat, and protein yield and fat and protein % with the Lod scores of 10.9, 2.3, 6.0, 25.4 and 3.2, respectively. The joint analyses including LD information by use of multi-marker haplotypes highly increased the evidence of the QTL (Lod scores were 25.1, 20.9, 11.0, 85.7 and 17.4 for the corresponding traits, respectively). The joint analyses including DGAT markers in the defined haplotypes again increased the QTL evidence and the most likely QTL positions for the five traits coincided with the position of the DGAT gene, supporting the hypothesis of the direct causal involvement of the DGAT gene. This study strongly indicates that the exploitation of LD information will allow additional gains of power and precision in finding and localising QTL of interest in livestock species, on the condition of high marker density around the QTL region.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.164-164
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• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.